Systems and methods for legal document generation

ABSTRACT

A system is configured to receive first training data, train a first neural network (NN) based on the first training data, receive second training data, train a second NN based on the second training data, receive a first plain English phrase, provide the first plain English phrase to the first NN, generate, via the first NN, one or more first legal clauses based on the first plain English phrase, receive a second plain English phrase, provide the second plain English phrase to the first NN, generate, via the first NN, one or more second legal clauses based on the second plain English phrase, provide the one or more first legal clauses and the one or more second legal clauses to the second NN, and generate, via the second NN, a legal document based on the one or more first legal clauses and the one or more second legal clauses.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of, and claims priority under 35U.S.C. § 120 to, U.S. patent application Ser. No. 16/277,324, filed Feb.15, 2019, which claims the benefit of U.S. Provisional Application No.62/776,954, filed Dec. 7, 2018, the entire contents and substance ofwhich are hereby fully incorporated by reference.

FIELD OF INVENTION

The present disclosure relates to systems and methods for generating alegal document from a first plain English phrase and a second plainEnglish phrase, and more particularly to systems and methods using twoneural networks (NNs) to generate a legal document from a first plainEnglish phrase and a second plain English phrase.

BACKGROUND

Creating legal documents can be very time consuming and potentiallychallenging, even for qualified legal professionals. Often, a lawyermust slowly work through a client email, write-up, or discussion toconvert each requested provision into a legally binding document. Suchan arduous task may be time-consuming and expensive for the client whowants the legally binding document.

Accordingly, there is a need for systems and methods for effectively andefficiently generating legal documents from plain English phrases.Embodiments of the present disclosure are directed to this and otherconsiderations.

SUMMARY

Disclosed embodiments provide systems and methods using two NNs forgenerating a legal document from plain English phrases.

Consistent with the disclosed embodiments, various methods and systemsare disclosed. In an embodiment, a method for generating a legaldocument based on a first plain English phrase and a second plainEnglish phrase is disclosed. The method may be implemented with acomputing device. The method may include receiving first training data.The method may include training a first NN based on the first trainingdata. The method may include receiving second training data. The methodmay include training a second NN based on the second training data. Themethod may include receiving a first plain English phrase. The methodmay include providing the first plain English phrase to the first NN.The method may include generating, via the first NN, one or more firstlegal clauses based on the first plain English phrase. The method mayinclude receiving a second plain English phrase. The method may includeproviding the second plain English phrase to the first NN. The methodmay include generating, via the first NN, one or more second legalclauses based on the second plain English phrase. The method may includeproviding the one or more first legal clauses and the one or more secondlegal clauses to the second NN. The method may include generating, viathe second NN, a legal document based on the one or more first legalclauses and the one or more second legal clauses.

Further features of the disclosed design, and the advantages offeredthereby, are explained in greater detail hereinafter with reference tospecific embodiments illustrated in the accompanying drawings, whereinlike elements are indicated be like reference designators.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and which are incorporated into andconstitute a portion of this disclosure, illustrate variousimplementations and aspects of the disclosed technology and, togetherwith the description, serve to explain the principles of the disclosedtechnology. In the drawings:

FIG. 1 is a diagram of an example system environment that may be used toimplement one or more embodiments of the present disclosure;

FIG. 2 is a component diagram of a service provider terminal accordingto an example embodiment;

FIG. 3 is a component diagram of a computing device according to anexample embodiment; and

FIG. 4A and FIG. 4B are flowcharts of a method for generating a legaldocument based on a first plain English phrase and a second plainEnglish phrase according to an example embodiment.

FIG. 5 is a flowchart of a method for generating a legal document basedon a first plain English phrase and a second plain English phraseaccording to an example embodiment.

DETAILED DESCRIPTION

Some implementations of the disclosed technology will be described morefully with reference to the accompanying drawings. This disclosedtechnology may, however, be embodied in many different forms and shouldnot be construed as limited to the implementations set forth herein. Thecomponents described hereinafter as making up various elements of thedisclosed technology are intended to be illustrative and notrestrictive. Many suitable components that would perform the same orsimilar functions as components described herein are intended to beembraced within the scope of the disclosed electronic devices andmethods. Such other components not described herein may include, but arenot limited to, for example, components developed after development ofthe disclosed technology.

It is also to be understood that the mention of one or more method stepsdoes not preclude the presence of additional method steps or interveningmethod steps between those steps expressly identified. Similarly, it isalso to be understood that the mention of one or more components in adevice or system does not preclude the presence of additional componentsor intervening components between those components expressly identified.

As used herein, the term “legalese” refers to the specialized languageof the legal profession.

This disclosure discusses using a first NN to generate one or more firstlegal clauses from a first plain English phrase one or more second legalclauses from a second plain English phrase. This disclose also discussesusing a second NN to generate a legal document from one or more firstlegal clauses and one or more second legal clauses. It is envisionedthat the first NN could be a recurrent neural network (RNN), aconvolutional neural network (CNN), or a recurrent convolutional neuralnetwork (RCNN). It is envisioned that the second NN could be a recurrentneural network (RNN), a convolutional neural network (CNN), or arecurrent convolutional neural network (RCNN).

An RNN takes in characters, words, or sentences one at a time. Each ofthe characters, words, or sentences are fed into the RNN one afteranother. The RNN has cells (e.g., long short-term memory units) that canremember prior characters, words, or sentences. The RNN model can detectchanges in data that changes in time, find patterns in text data, etc.In contrast, a CNN takes in all characters, words, or sentences at oncemaking CNNs faster at computing than RNNs. Thus, CNN may be better attranslating a paragraph to a sentence. However, the CNN cannot rememberwhat happened before the paragraph since it takes all of the characters,words, or sentences in at once. The RCNN is some combination of an RNNand a CNN. Typically, the RNN will accept the output of a CNN in theRCNN.

The present disclosure relates to methods and systems for using two NNs,and, in particular, for utilizing the two NNs to generate a legaldocument (e.g., assignment of interest, non-disclosure agreement,employment contract, terms of service agreement) from plain Englishphrases. In some embodiments, a method may include receiving firsttraining data, training a first NN based on the first training data,receiving second training data, and training a second NN based on thesecond training data. The method may also include receiving a firstplain English phrase, providing the first plain English phrase to thefirst NN, and generating, via the first NN, one or more first legalclauses based on the first plain English phrase. The method may alsoinclude receiving a second plain English phrase, providing the secondplain English phrase to the first NN, generating, via the first NN, oneor more second legal clauses based on the second plain English phrase.The method may further include providing the one or more first legalclauses and the one or more second legal clause to the second NN andgenerating, via the second NN, a legal document based on the one or morefirst legal clauses and the one or more second legal clauses.

Reference will now be made in detail to example embodiments of thedisclosed technology, examples of which are illustrated in theaccompanying drawings and disclosed herein. Wherever convenient, thesame references numbers will be used throughout the drawings to refer tothe same or like parts.

FIG. 1 is a diagram of an example system environment that may be used toimplement one or more embodiments of the present disclosure. Thecomponents and arrangements shown in FIG. 1 are not intended to limitthe disclosed embodiments as the components used to implement thedisclosed processes and features may vary.

In accordance with disclosed embodiments, system 100 may include aservice provider system 110 in communication with a computing device 120via network 105. In some embodiments, service provider system 110 mayalso be in communication with various databases. Computing device 120may be a mobile computing device (e.g., a smart phone, tablet computer,smart wearable device, portable laptop computer, voice command device,wearable augmented reality device, or other mobile computing device) ora stationary device (e.g., desktop computer).

In some embodiments, the computing device 120 may transmit a first plainEnglish phrase and a second plain English phrase to the service providersystem 110, and the service provider system 110 may utilize a first NNto generate one or more first legal clauses based on the first plainEnglish phrase and one or more second legal clauses based on the secondplain English phrase. In other words, the service provider system, usinga first NN, may translate one or more plain English phrases to one ormore legal clauses. In some embodiments, the server provider terminal110 may control the computing device 120 to implement one or moreaspects of the first NN. In some embodiments, these service providerterminal 110 may using a second NN, generate a legal document from theone or more first legal clauses and the one or more second legalclauses.

Network 105 may be of any suitable type, including individualconnections via the internet such as cellular or WiFi networks. In someembodiments, network 105 may connect terminals using direct connectionssuch as radio-frequency identification (RFID), near-field communication(NFC), Bluetooth™, low-energy Bluetooth™ (BLE), WiFi™, ZigBee™ ambientbackscatter communications (ABC) protocols, USB, or LAN. Because theinformation transmitted may be personal or confidential, securityconcerns may dictate one or more of these types of connections beencrypted or otherwise secured. In some embodiments, however, theinformation being transmitted may be less personal, and therefore thenetwork connections may be selected for convenience over security.

An example embodiment of service provider system 110 is shown in moredetail in FIG. 2. Computing device 120 may have a similar structure andcomponents that are similar to those described with respect to serviceprovider system 110. As shown, service provider system 110 may include aprocessor 210, an input/output (“I/O”) device 220, a memory 230containing an operating system (“OS”) 240 and a program 250. Forexample, service provider system 110 may be a single server or may beconfigured as a distributed computer system including multiple serversor computers that interoperate to perform one or more of the processesand functionalities associated with the disclosed embodiments. In someembodiments, service provider system 110 may further include aperipheral interface, a transceiver, a mobile network interface incommunication with processor 210, a bus configured to facilitatecommunication between the various components of the service providersystem 110, and a power source configured to power one or morecomponents of service provider system 110.

A peripheral interface may include the hardware, firmware and/orsoftware that enables communication with various peripheral devices,such as media drives (e.g., magnetic disk, solid state, or optical diskdrives), other processing devices, or any other input source used inconnection with the instant techniques. In some embodiments, aperipheral interface may include a serial port, a parallel port, ageneral-purpose input and output (GPIO) port, a game port, a universalserial bus (USB), a micro-USB port, a high definition multimedia (HDMI)port, a video port, an audio port, a Bluetooth™ port, a near-fieldcommunication (NFC) port, another like communication interface, or anycombination thereof.

In some embodiments, a transceiver may be configured to communicate withcompatible devices and ID tags when they are within a predeterminedrange. A transceiver may be compatible with one or more of:radio-frequency identification (RFID), near-field communication (NFC),Bluetooth™, low-energy Bluetooth™ (BLE), WiFi™, ZigBee™ ambientbackscatter communications (ABC) protocols or similar technologies.

A mobile network interface may provide access to a cellular network, theInternet, or another wide-area network. In some embodiments, a mobilenetwork interface may include hardware, firmware, and/or software thatallows processor(s) 210 to communicate with other devices via wired orwireless networks, whether local or wide area, private or public, asknown in the art. A power source may be configured to provide anappropriate alternating current (AC) or direct current (DC) to powercomponents.

As described above, service provider system 110 may configured toremotely communicate with one or more other devices, such as computerdevice 120. According to some embodiments, service provider system 110may utilize an NN to translate a plain English request to a legal clausein legalese.

Processor 210 may include one or more of a microprocessor,microcontroller, digital signal processor, co-processor or the like orcombinations thereof capable of executing stored instructions andoperating upon stored data. Memory 230 may include, in someimplementations, one or more suitable types of memory (e.g. such asvolatile or non-volatile memory, random access memory (RAM), read onlymemory (ROM), programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), magnetic disks, optical disks,floppy disks, hard disks, removable cartridges, flash memory, aredundant array of independent disks (RAID), and the like), for storingfiles including an operating system, application programs (including,for example, a web browser application, a widget or gadget engine, andor other applications, as necessary), executable instructions and data.In one embodiment, the processing techniques described herein areimplemented as a combination of executable instructions and data withinthe memory 230.

Processor 210 may be one or more known processing devices, such as amicroprocessor from the Pentium™ family manufactured by Intel™ or theTurion™ family manufactured by AMD™. Processor 210 may constitute asingle core or multiple core processor that executes parallel processessimultaneously. For example, processor 210 may be a single coreprocessor that is configured with virtual processing technologies. Incertain embodiments, processor 210 may use logical processors tosimultaneously execute and control multiple processes. Processor 210 mayimplement virtual machine technologies, or other similar knowntechnologies to provide the ability to execute, control, run,manipulate, store, etc. multiple software processes, applications,programs, etc. One of ordinary skill in the art would understand thatother types of processor arrangements could be implemented that providefor the capabilities disclosed herein.

Service provider system 110 may include one or more storage devicesconfigured to store information used by processor 210 (or othercomponents) to perform certain functions related to the disclosedembodiments. In one example, service provider system 110 may includememory 230 that includes instructions to enable processor 210 to executeone or more applications, such as server applications, networkcommunication processes, and any other type of application or softwareknown to be available on computer systems. Alternatively, theinstructions, application programs, etc. may be stored in an externalstorage or available from a memory over a network. The one or morestorage devices may be a volatile or non-volatile, magnetic,semiconductor, tape, optical, removable, non-removable, or other type ofstorage device or tangible computer-readable medium.

In one embodiment, service provider system 110 may include memory 230that includes instructions that, when executed by processor 210, performone or more processes consistent with the functionalities disclosedherein. Methods, systems, and articles of manufacture consistent withdisclosed embodiments are not limited to separate programs or computersconfigured to perform dedicated tasks. For example, service providersystem 110 may include memory 230 that may include one or more programs250 to perform one or more functions of the disclosed embodiments.Moreover, processor 210 may execute one or more programs 250 locatedremotely from service provider system 110. For example, service providersystem 110 may access one or more remote programs 250, that, whenexecuted, perform functions related to disclosed embodiments.

Memory 230 may include one or more memory devices that store data andinstructions used to perform one or more features of the disclosedembodiments. Memory 230 may also include any combination of one or moredatabases controlled by memory controller devices (e.g., server(s),etc.) or software, such as document management systems, Microsoft™ SQLdatabases, SharePoint™ databases, Oracle™ databases, Sybase™ databases,or other relational databases. Memory 230 may include softwarecomponents that, when executed by processor 210, perform one or moreprocesses consistent with the disclosed embodiments. In someembodiments, memory 230 may include an image processing database 260 anda neural-network pipeline database 270 for storing related data toenable service provider system 110 to perform one or more of theprocesses and functionalities associated with the disclosed embodiments.

Service provider system 110 may also be communicatively connected to oneor more memory devices (e.g., databases (not shown)) locally or througha network. The remote memory devices may be configured to storeinformation and may be accessed and/or managed by service providersystem 110. By way of example, the remote memory devices may be documentmanagement systems, Microsoft™ SQL database, SharePoint™ databases,Oracle™ databases, Sybase™ databases, or other relational databases.Systems and methods consistent with disclosed embodiments, however, arenot limited to separate databases or even to the use of a database.

Service provider system 110 may also include one or more I/O devices 220that may include one or more interfaces for receiving signals or inputfrom devices and providing signals or output to one or more devices thatallow data to be received and/or transmitted by service provider system110. For example, service provider system 110 may include interfacecomponents, which may provide interfaces to one or more input devices,such as one or more keyboards, mouse devices, touch screens, track pads,trackballs, scroll wheels, digital cameras, microphones, sensors, andthe like, that enable service provider system 110 to receive data fromone or more users (such as via computing device 120).

In example embodiments of the disclosed technology, service providersystem 110 may include any number of hardware and/or softwareapplications that are executed to facilitate any of the operations. Theone or more I/O interfaces may be utilized to receive or collect dataand/or user instructions from a wide variety of input devices. Receiveddata may be processed by one or more computer processors as desired invarious implementations of the disclosed technology and/or stored in oneor more memory devices.

While service provider system 110 has been described as one form forimplementing the techniques described herein, those having ordinaryskill in the art will appreciate that other, functionally equivalenttechniques may be employed. For example, as known in the art, some orall of the functionality implemented via executable instructions mayalso be implemented using firmware and/or hardware devices such asapplication specific integrated circuits (ASICs), programmable logicarrays, state machines, etc. Furthermore, other implementations of theterminal 110 may include a greater or lesser number of components thanthose illustrated.

FIG. 3 shows an example embodiment of computing device 120. As shown,computing device 120 may include input/output (“I/O”) device 220 forreceiving data from another device (e.g., service provider system 110),memory 230 containing operating system (“OS”) 240, program 250, and anyother associated component as described above with respect to serviceprovider system 110. Computing device 120 may also have one or moreprocessors 210, a geographic location sensor (“GLS”) 304 for determiningthe geographic location of computing device 120, a display 306 fordisplaying content such as text messages, images, and selectablebuttons/icons/links, an environmental data (“ED”) sensor 308 forobtaining environmental data including audio and/or visual information,and a user interface (“U/I”) device 310 for receiving user input data,such as data representative of a click, a scroll, a tap, a press, ortyping on an input device that can detect tactile inputs. User inputdata may also be non-tactile inputs that may be otherwise detected by EDsensor 308. For example, user input data may include auditory commands.According to some embodiments, U/I device 310 may include some or all ofthe components described with respect to input/output device 220 above.In some embodiments, environmental data sensor 308 may include amicrophone and/or an image capture device, such as a digital camera.

FIG. 4A and FIG. 4B show flowcharts of a method 400 for generating alegal document based on a first plain English phrase and a second plainEnglish phrase according to an example embodiment. Method 400 may beperformed by one or more of the service provider system 110 and thecomputing device 120.

In block 402, the system may receive first training data. The firsttraining data may be plain English phrases paired with correspondinglegal clauses.

In block 404, the system may train a first neural network (NN) based onthe first training data. In other words, the system may provide theEnglish phrases and corresponding legal clauses to the first NN. Asdiscussed above, the first NN may be a recurrent neural network (RNN), aconvolutional neural network (CNN), or a recurrent convolutional neuralnetwork (RCNN).

In block 406, the system may receive second training data. The secondtraining data may be legal clauses paired with a corresponding legaldocument including all the legal clauses.

In block 408, the system may train a second neural network (NN) based onthe second training data. In other words, the system may provide thelegal clauses and corresponding legal document to the second NN. Asdiscussed above, the second NN may be an RNN, a CNN, or an RCNN.

In block 410, the system may receive a first plain English phrase. Thesystem may provide text box as a frontend user interface that receives auser input. The first plain English phrase may be a request for a clauseof a non-disclosure agreement (NDA). For example, the first plainEnglish phrase received from a user may be:

-   -   I would like a clause for an NDA stating that all written        confidential materials be returned within three days upon a        written request from our company and stating that the receiving        party must hold the confidential material in confidence until        released by us.

In block 412, the system may provide the first plain English phrase tothe first NN. For example, the system may utilize an applicationprogramming interface (API) call or pass the text of the first plainEnglish phrase to the NN.

In block 414, the system may generate, via the first NN, one or morefirst legal clauses based on the first plain English phrase. In somecases, the service provider system 110 generates the one or more firstlegal clauses. In other cases, the computing device 120 generates theone or more first legal clauses. In either case, a K-nearest neighboralgorithm or the first NN may be used, which may be an RNN or a CNN. TheRNN, a CNN, or a K-nearest neighbor algorithm would predict how closelya request matches a potential model (e.g., NDA model) prior togenerating the one or more first legal clauses. The one or more firstlegal clauses may include the following:

-   -   Upon the written request of the Disclosing Party, the Receiving        Party shall return to the Disclosing Party all written materials        containing or related to the Confidential Information within        three days from the written request.    -   The Receiving Party shall keep the Confidential Information        confidential until the Receiving Party receives a written notice        from the Disclosing Party releasing the Receiving Party from the        Receiving Party's confidentiality obligation.

In block 416, the system may receive a second plain English phrase. Thesecond plain English phrase may be a request for a second clause in anNDA. For example, the second plain English phrase may be “I would like aclause for an NDA that deems our source code as confidential.”

In block 418, the system may provide the second plain English phrase tothe first NN. The system may utilize an API call or pass the text of thesecond plain English phrase to the NN.

In block 420, the system may generate, via the first NN, one or moresecond legal clauses based on the second plain English phrase. Forexample, the one or more second legal clauses may include the following:

-   -   “Confidential Information” shall include any data or information        that has commercial value or other utility in business in which        the Disclosing Party is engaged. Confidential Information        specifically includes, but is not limited to, the Disclosing        Party's source code and any information related to that source        code.

In block 422, the system may provide the one or more first legal clausesand the one or more second legal clauses to the second NN. The systemmay simply pass the text of the one or more first legal clauses and thetext of the one or more second legal clauses to the second NN.

In block 424, the system may generate, via the second NN, a legaldocument based on the one or more first legal clauses and the one ormore second legal clauses. The second NN may be an RNN which utilizessentence embeddings to generate the legal document from the one or morefirst legal clauses and the one or more second legal clauses. The legaldocument may be an NDA with the requested provisions as well as otherboilerplate provisions such as choice of law provisions, severabilityprovisions, etc.

The system may receive a correction of the legal document from a user.The system may iteratively re-train the first NN, the second NN, or boththe first NN and the second NN based on the correction. For example, aregistered user (who may be an attorney or other qualified legalprofessional) may review the legal document and the one or more legalclauses within the legal document. Upon review, a user may submit acorrection to the service provider system 110 from a computing device120 via the network 105. The correction may take the form of a negativereinforcement by submitting a corrected version of the one or more firstlegal clauses and/or the one or more second legal clauses. In someembodiments, the user (e.g., computing device 120) may submit positivereinforcement feedback to the service provider system 110. The positivereinforcement feedback would simply be confirming that the one or morefirst legal clauses and/or the one or more second legal clauses areaccurate.

FIG. 5 shows a flowchart of a method 500 for generating a legal documentbased on a first plain English phrase and a second plain English phraseaccording to an example embodiment. Method 500 may be performed by oneor more of the service provider system 110 and the computing device 120.

In method 500, blocks 510, 512, 514, 516, 520, 522, and 524 may be thesame as or similar to blocks 410, 412, 416, 420, 422, 424, respectively,thus their descriptions will not be repeated for brevity.

Certain implementations provide the advantage of converting plainEnglish to legal clauses. Thus, certain implementations make it easierto generate legal documents.

As used in this application, the terms “component,” “module,” “system,”“server,” “processor,” “memory,” and the like are intended to includeone or more computer-related units, such as but not limited to hardware,firmware, a combination of hardware and software, software, or softwarein execution. For example, a component may be, but is not limited tobeing, a process running on a processor, an object, an executable, athread of execution, a program, and/or a computer. By way ofillustration, both an application running on a computing device and thecomputing device can be a component. One or more components can residewithin a process and/or thread of execution and a component may belocalized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate by way of local and/or remote processessuch as in accordance with a signal having one or more data packets,such as data from one component interacting with another component in alocal system, distributed system, and/or across a network such as theInternet with other systems by way of the signal.

Certain embodiments and implementations of the disclosed technology aredescribed above with reference to block and flow diagrams of systems andmethods and/or computer program products according to exampleembodiments or implementations of the disclosed technology. It will beunderstood that one or more blocks of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, respectively, can be implemented by computer-executableprogram instructions. Likewise, some blocks of the block diagrams andflow diagrams may not necessarily need to be performed in the orderpresented, may be repeated, or may not necessarily need to be performedat all, according to some embodiments or implementations of thedisclosed technology.

These computer-executable program instructions may be loaded onto ageneral-purpose computer, a special-purpose computer, a processor, orother programmable data processing apparatus to produce a particularmachine, such that the instructions that execute on the computer,processor, or other programmable data processing apparatus create meansfor implementing one or more functions specified in the flow diagramblock or blocks. These computer program instructions may also be storedin a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement one or more functions specified in the flow diagram blockor blocks.

As an example, embodiments or implementations of the disclosedtechnology may provide for a computer program product, including acomputer-usable medium having a computer-readable program code orprogram instructions embodied therein, said computer-readable programcode adapted to be executed to implement one or more functions specifiedin the flow diagram block or blocks. Likewise, the computer programinstructions may be loaded onto a computer or other programmable dataprocessing apparatus to cause a series of operational elements or stepsto be performed on the computer or other programmable apparatus toproduce a computer-implemented process such that the instructions thatexecute on the computer or other programmable apparatus provide elementsor steps for implementing the functions specified in the flow diagramblock or blocks.

Accordingly, blocks of the block diagrams and flow diagrams supportcombinations of means for performing the specified functions,combinations of elements or steps for performing the specifiedfunctions, and program instruction means for performing the specifiedfunctions. It will also be understood that each block of the blockdiagrams and flow diagrams, and combinations of blocks in the blockdiagrams and flow diagrams, can be implemented by special-purpose,hardware-based computer systems that perform the specified functions,elements or steps, or combinations of special-purpose hardware andcomputer instructions.

Certain implementations of the disclosed technology are described abovewith reference to user devices may include mobile computing devices.Those skilled in the art recognize that there are several categories ofmobile devices, generally known as portable computing devices that canrun on batteries but are not usually classified as laptops. For example,mobile devices can include, but are not limited to portable computers,tablet PCs, internet tablets, PDAs, ultra-mobile PCs (UMPCs), wearabledevices, and smart phones. Additionally, implementations of thedisclosed technology can be utilized with internet of things (IoT)devices, smart televisions and media devices, appliances, automobiles,toys, and voice command devices, along with peripherals that interfacewith these devices.

In this description, numerous specific details have been set forth. Itis to be understood, however, that implementations of the disclosedtechnology may be practiced without these specific details. In otherinstances, well-known methods, structures and techniques have not beenshown in detail in order not to obscure an understanding of thisdescription. References to “one embodiment,” “an embodiment,” “someembodiments,” “example embodiment,” “various embodiments,” “oneimplementation,” “an implementation,” “example implementation,” “variousimplementations,” “some implementations,” etc., indicate that theimplementation(s) of the disclosed technology so described may include aparticular feature, structure, or characteristic, but not everyimplementation necessarily includes the particular feature, structure,or characteristic. Further, repeated use of the phrase “in oneimplementation” does not necessarily refer to the same implementation,although it may.

Throughout the specification and the claims, the following terms take atleast the meanings explicitly associated herein, unless the contextclearly dictates otherwise. The term “connected” means that onefunction, feature, structure, or characteristic is directly joined to orin communication with another function, feature, structure, orcharacteristic. The term “coupled” means that one function, feature,structure, or characteristic is directly or indirectly joined to or incommunication with another function, feature, structure, orcharacteristic. The term “or” is intended to mean an inclusive “or.”Further, the terms “a,” “an,” and “the” are intended to mean one or moreunless specified otherwise or clear from the context to be directed to asingular form. By “comprising” or “containing” or “including” is meantthat at least the named element, or method step is present in article ormethod, but does not exclude the presence of other elements or methodsteps, even if the other such elements or method steps have the samefunction as what is named.

As used herein, unless otherwise specified the use of the ordinaladjectives “first,” “second,” “third,” etc., to describe a commonobject, merely indicate that different instances of like objects arebeing referred to, and are not intended to imply that the objects sodescribed must be in a given sequence, either temporally, spatially, inranking, or in any other manner.

While certain embodiments of this disclosure have been described inconnection with what is presently considered to be the most practicaland various embodiments, it is to be understood that this disclosure isnot to be limited to the disclosed embodiments, but on the contrary, isintended to cover various modifications and equivalent arrangementsincluded within the scope of the appended claims. Although specificterms are employed herein, they are used in a generic and descriptivesense only and not for purposes of limitation.

This written description uses examples to disclose certain embodimentsof the technology and also to enable any person skilled in the art topractice certain embodiments of this technology, including making andusing any apparatuses or systems and performing any incorporatedmethods. The patentable scope of certain embodiments of the technologyis defined in the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral language of the claims.

Example Use Case

The following example use case describes an example of a typical use ofgenerating a legal document based on a first plain English phrase and asecond plain English phrase. It is intended solely for explanatorypurposes and not in limitation. In one case, after two RNN are trainedfrom first training data and second training data, a user types intotheir portable laptop computer (e.g., computing device 120) a firstplain English phrase such as “I would like a clause for an NDA statingthat all written confidential materials be returned within three daysupon a written request from our company and stating that the receivingparty must hold the confidential material in confidence until releasedby us . . . .” The user then sends the first plain English phrase to theservice provider system 110 via network 105 and computing device 120.The service provider system 110 then provides the plain English phraseto a first RNN. The service provider system 110, via the first RNN,generates one or more first legal clauses. For example, the serviceprovider system 110, via the first RNN, may generate the following

-   -   Upon the written request of the Disclosing Party, the Receiving        Party shall return to the Disclosing Party all written materials        containing or related to the Confidential Information within        three days from the written request.    -   The Receiving Party shall keep the Confidential Information        confidential until the Receiving Party receives a written notice        from the Disclosing Party releasing the Receiving Party from the        Receiving Party's confidentiality obligation.

The user types into their portable laptop computer (e.g., computingdevice 120) a second plain English phrase such as “I would like a clausefor an NDA that deems our source code as confidential.” The user thensends the second plain English phrase to the service provider system 110via network 105 and computing device 120. The service provider system110 then provides the plain English phrase to a first RNN. The serviceprovider system 110, via the first RNN, generates one or more secondlegal clauses. The one or more second legal clauses may include thefollowing:

-   -   “Confidential Information” shall include any data or information        that has commercial value or other utility in business in which        Disclosing Party is engaged. Confidential Information        specifically includes, but is not limited to, the Disclosing        Party's source code and any information related to that source        code.

The service provider system 110 then provides the one or more firstlegal clauses and the one or more second legal clauses to a second RNN.The second RNN utilizes sentence embeddings to generate a legal documentbased on the one or more first legal clauses, the one or more secondlegal clauses, and a database of boilerplate clauses for NDAs.

In some use cases, the service provider system 110 receives a correctionof the legal document from a user. For example, the user may type into achat window “The legal document should say that our proprietaryinformation should be confidential.” The service provider system 110then may iteratively re-train first NN to make sure that the definitionof Confidential Information for an NDA clause includes proprietaryinformation.

What is claimed is:
 1. A system, comprising: one or more processors; anda memory in communication with the one or more processors and storinginstructions that, when executed by the one or more processors, areconfigured to cause the system to: receive first training data; train afirst neural network (NN) based on the first training data; receivesecond training data; train a second NN based on the second trainingdata; receive a third training data; train a third NN based on the thirdtraining data; receive a first phrase; determine whether the firstphrase matches the first NN or the third NN; provide the first phrase tothe first NN or the third NN based on determining whether the firstphrase corresponds with the first NN or the third NN; generate, via thefirst NN or the third NN, one or more first legal clauses based on thefirst phrase; receive a second phrase; determine whether the secondphrase corresponds with the first NN or the third NN; provide the secondphrase to the first NN or the third NN based on determining whether thesecond phrase corresponds with the first NN or the third NN; generate,via the first NN or the third NN, one or more second legal clauses basedon the second phrase; provide the one or more first legal clauses andthe one or more second legal clauses to the second NN; and generate, viathe second NN, a legal document based on the one or more first legalclauses and the one or more second legal clauses.
 2. The system of claim1, wherein the memory storing further instructions configured to causethe system to: receive one or more corrected first legal clauses from auser device; receive a confirmation that the one or more second legalclauses are accurate from the user device; re-train the first NN or thethird NN based on the one or more corrected first legal clauses receivedfrom the user device; and re-train the second NN based on theconfirmation received from the user device.
 3. The system of claim 1,wherein the legal document generated is a terms of service document or anon-disclosure agreement.
 4. The system of claim 1, wherein: firsttraining data comprises phrases paired with respective legal clauses,and the second training data comprises legal clauses and correspondinglegal documents.
 5. The system of claim 1, wherein the first NN is afirst recurrent neural network (RNN), the second NN is a convolutionalneural network (CNN), and the third NN is a second recurrent neuralnetwork trained with different training data than the first recurrentneural network.
 6. The system of claim 1, wherein the first NN is afirst recurrent neural network (RNN), the second NN is a second RNN, andthe third NN is a third RNN.
 7. The system of claim 6, wherein thesecond RNN utilizes sentence embeddings to generate the legal documentfrom the one or more first legal clauses and the one or more secondlegal clauses.
 8. A method for generating a legal document, comprising:receiving first training data; training a first neural network (NN)based on the first training data; receiving second training data;training a second NN based on the second training data; receiving athird training data; training a third NN based on the third trainingdata; receiving a first plain English phrase; determining whether thefirst phrase corresponds with the first NN or the third NN; providingthe first phrase to the first NN or the third NN based on determiningwhether the first phrase corresponds with the first NN or the third NN;generating, via the first NN or the third NN, one or more first legalclauses based on the first phrase; receiving a second phrase;determining whether the second phrase matches the first NN or the thirdNN; providing the second phrase to the first NN or the third NN based ondetermining whether the second phrase corresponds with the first NN orthe third NN; generating, via the first NN or the third NN, one or moresecond legal clauses based on the second phrase; providing the one ormore first legal clauses and the one or more second legal clauses to thesecond NN; and generating, via the second NN, the legal document basedon the one or more first legal clauses and the one or more second legalclauses.
 9. The method of claim 1, further comprising: receiving one ormore corrected first legal clauses from a user device; receiving aconfirmation that the one or more second legal clauses are accurate fromthe user device; re-training the first NN or the third NN based on theone or more corrected first legal clauses received from the user device;and re-training the second NN based on the confirmation received fromthe user device
 10. The method of claim 8, wherein: the legal documentgenerated is a terms of service document or a non-disclosure agreement,and first training data comprises plain English phrases paired withrespective legal clauses.
 11. The method of claim 10, wherein the secondtraining data comprises legal clauses and corresponding legal documents.12. The method of claim 8, wherein the first NN is a first recurrentneural network (RNN), the second NN is a convolutional neural network(CNN), and the third NN is a second RNN.
 13. The method of claim 8,wherein the first NN is a first recurrent neural network (RNN), thesecond NN is a second RNN, and the third NN is a third RNN.
 14. Themethod of claim 13, wherein the second RNN utilizes word embeddings andsentence embeddings to generate the legal document from the one or morefirst legal clauses and the one or more second legal clauses.
 15. Asystem, comprising: one or more processors; and a memory incommunication with the one or more processors and storing instructionsthat, when executed by the one or more processors, are configured tocause the system to: receive a first phrase; determine whether the firstplain English phrase corresponds with a first NN or a third NN; providethe first phrase to the first NN or the third NN based on determiningwhether the first phrase corresponds with the first NN or the third NN;generate, via the first NN or the third NN, one or more first legalclauses based on the first phrase; receive a second phrase; determinewhether the second phrase corresponds with the first NN or the third NN;provide the second phrase to the first NN or the third NN based ondetermining whether the second phrase corresponds with the first NN orthe third NN; generate, via the first NN or the third NN, one or moresecond legal clauses based on the second phrase; provide the one or morefirst legal clauses and the one or more second legal clauses to a secondNN; and generate, via the second NN, a legal document based on the oneor more first legal clauses and the one or more second legal clauses.16. The system of claim 15, wherein the memory storing furtherinstructions configured to cause the system to: receive one or morecorrected first legal clauses from a user device; receive a confirmationthat the one or more second legal clauses are accurate from the userdevice; train the first NN based on the one or more corrected firstlegal clauses received from the user device; and train the second NNbased on the confirmation received from the user device.
 17. The systemof claim 15, wherein the first NN is a first recurrent neural network(RNN), the second NN is a convolutional neural network (CNN), and thethird NN is a second RNN.
 18. The system of claim 15, wherein the firstNN is a first recurrent neural network (RNN), the second NN is a secondRNN, and the third NN is a third RNN.
 19. The system of claim 18,wherein the second RNN utilizes sentence embeddings to generate thelegal document from the one or more first legal clauses and the one ormore second legal clauses.
 20. The system of claim 15, wherein the firstphrase is a request to draft one or more particular clauses for the oneor more first legal clauses.